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Robot programming typically makes use of a set of mechanical skills that isacquired by machine learning. Because there is in general no guarantee thatmachine learning produces robot programs that are free of surprising behavior,the safe execution of a robot program must utilize monitoring modules that takesensor data as inputs in real time to ensure the correctness of the skillexecution. Owing to the fact that sensors and monitoring algorithms are usuallysubject to physical restrictions and that effective robot programming issensitive to the selection of skill parameters, these considerations may leadto different sensor input qualities such as the view coverage of a visionsystem that determines whether a skill can be successfully deployed inperforming a task. Choosing improper skill parameters may cause the monitoringmodules to delay or miss the detection of important events such as a mechanicalfailure. These failures may reduce the throughput in robotic manufacturing andcould even cause a destructive system crash. To address above issues, wepropose a sensing quality-aware robot programming system that automaticallycomputes the sensing qualities as a function of the robot's environment anduses the information to guide non-expert users to select proper skillparameters in the programming phase. We demonstrate our system framework on a6DOF robot arm for an object pick-up task.

SQRP: Sensing Quality-aware Robot Programming System for Non-expert Programmers